A physician can identify when they are uncertain about a case and can access other sources of information (e.g. additional tests, medical history, etc). On the other hand, deep learning methods are have been proposed without specific measures to access their uncertainty. This leads to the challenge of calibrating user trust [1]} on the system and consequently reduced acceptance. The samples with high uncertainty can be referred to a human expert to reduce mistakes. Previous studies have indicated that uncertain cases are strongly correlated with errors and identifying them increased model performance e.g. [2]}. There are some recent studies combining uncertainty and explainability information for 3D object detection [3]} and clinical time serizes prediction [4]}, [5]}.
